{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TCHEM_INSTALL_PATH/example/runs/T-CSTR/CH4_PT_Quinceno2006'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n",
    "from mpl_toolkits.axes_grid1.inset_locator import mark_inset\n",
    "from mpl_toolkits.axes_grid1 import host_subplot\n",
    "import mpl_toolkits.axisartist as AA\n",
    "import numpy as np\n",
    "import os\n",
    "os.getcwd() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.genfromtxt(\"CSTRSolutionODE.dat\", dtype=str)\n",
    "Header = (data[0,:]).tolist()\n",
    "solTchem = (data[1:,:]).astype(np.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makefigure3(x, y1, y2, y3, info, figure_name='TempDensVelPFR' ):\n",
    "    \n",
    "    label1 = info['label1']['label']\n",
    "    label2 = info['label2']['label']\n",
    "    label3 = info['label3']['label']\n",
    "    \n",
    "    ylabel1 = label1 + info['label1']['units']\n",
    "    ylabel2 = info['label2']['units']\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    host = AA.Axes(fig, [0.1, 0.1, 0.8, 0.8]) \n",
    "    fig.add_axes(host)\n",
    "\n",
    "    par1 = host.twinx()\n",
    "\n",
    "    host.set_xlabel(\"Time [s]\")\n",
    "    host.set_ylabel(ylabel1)\n",
    "    par1.set_ylabel(ylabel2)\n",
    "\n",
    "    p1 = host.plot(x, y1,'r-', label=label1)\n",
    "    p2 = par1.plot(x, y2,'b-', label=label2)\n",
    "    p3 = par1.plot(x, y3,'g-', label=label3)\n",
    "\n",
    "    loc_x = info['loc_x']\n",
    "    loc_y = info['loc_y']\n",
    "    \n",
    "    par1.set_xlim(info['xlim'])\n",
    "\n",
    "    # added these three lines\n",
    "    lns = p1+p2+p3\n",
    "    labs = [l.get_label() for l in lns]\n",
    "    host.legend(lns, labs, bbox_to_anchor=(loc_x, loc_y),frameon=False)\n",
    "\n",
    "    inset = AA.Axes(fig, [info['inset_x1'], info['inset_y1'], info['inset_x2'], info['inset_y2']]) \n",
    "    fig.add_axes(inset)\n",
    "    inset.plot(x, y1,'r.-')\n",
    "    inset.set_yticks([])\n",
    "    par2 = inset.twinx()\n",
    "    par2.set_yticks([])\n",
    "    par2.plot(x, y2,'b.-')\n",
    "    par2.plot(x, y3,'g.-')\n",
    "\n",
    "    inset.set_xlim(info['xlim2'])\n",
    "    \n",
    "    mark_inset(host, inset, loc1=2, loc2=4, fc=\"none\", ec=\"0.75\")\n",
    "        \n",
    "    plt.xticks(visible=False)\n",
    "    plt.yticks(visible=False)\n",
    "    \n",
    "    plt.savefig(figure_name,bbox_inches='tight')\n",
    "    return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makePlotV2(x, y1, y2, y3, info, fig_name):\n",
    "    loc_x = info['loc_x']\n",
    "    loc_y = info['loc_y']\n",
    "    \n",
    "    ys1 = info['label1']['scale']\n",
    "    ys2 = info['label2']['scale']\n",
    "    ys3 = info['label3']['scale']\n",
    "    \n",
    "    if ys1== 1:\n",
    "        label1 = info['label1']['label']\n",
    "    else:    \n",
    "        label1 = info['label1']['label'] + ' x'+str(ys1)\n",
    "        \n",
    "    if ys2== 1:\n",
    "        label2 = info['label2']['label']\n",
    "    else:    \n",
    "        label2 = info['label2']['label'] + ' x'+str(ys2)\n",
    "        \n",
    "    if ys3== 1: \n",
    "        label3 = info['label3']['label']\n",
    "    else:     \n",
    "        label3 = info['label3']['label'] + ' x'+str(ys3)\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=[5,4])\n",
    "    p1 = ax.plot(x, y1/ys1,'r-',label = label1)\n",
    "    p2 = ax.plot(x, y2/ys2,'g-',label = label2)\n",
    "    p3 = ax.plot(x, y3/ys3,'b-',label = label3)\n",
    "    plt.xlabel(info['xlabel'])\n",
    "    plt.ylabel(info['ylabel'])\n",
    "    \n",
    "    plt.xlim(info['xlim'])\n",
    "    plt.ylim(info['ylim'])\n",
    "    \n",
    "    \n",
    "#     axins = zoomed_inset_axes(ax, info['zoom_scale'], loc =1) # zoom = 2\n",
    "    if info['add_zoom'] == True:\n",
    "        axins = ax.inset_axes(info['loc_zoom'])\n",
    "#     axins = ax.inset_axes(ax, 1,1 , loc=2,bbox_to_anchor=(0.2, 0.55),bbox_transform=ax.figure.transFigure)\n",
    "        axins.plot(x,y1/ys1,'r.-')\n",
    "        axins.plot(x,y2/ys2,'g.-')\n",
    "        axins.plot(x,y3/ys3,'b.-')\n",
    "    \n",
    "    # sub region of the original image\n",
    "        x1, x2, y1s, y2s = info['zoom']\n",
    "        axins.set_xlim(x1, x2)\n",
    "        axins.set_ylim(y1s, y2s)\n",
    "        axins.set_yticks([])\n",
    "        axins.set_xticks([])\n",
    "    # added these three lines\n",
    "        lns = p1+p2+p3\n",
    "        labs = [l.get_label() for l in lns]\n",
    "        ax.legend(lns, labs, bbox_to_anchor=(loc_x, loc_y),frameon=False)\n",
    "    \n",
    "    \n",
    "#     axins.xticks(visible=False)\n",
    "#     axins.yticks(visible=False)\n",
    "    \n",
    "    # draw a bbox of the region of the inset axes in the parent axes and\n",
    "    # connecting lines between the bbox and the inset axes area\n",
    "        mark_inset(ax, axins, loc1=2, loc2=4, fc=\"none\", ec=\"0.75\")\n",
    "    else:\n",
    "        lns = p1+p2+p3\n",
    "        labs = [l.get_label() for l in lns]\n",
    "        ax.legend(lns, labs, bbox_to_anchor=(loc_x, loc_y),frameon=False)\n",
    "    plt.draw()\n",
    "    plt.savefig(fig_name,bbox_inches='tight')\n",
    "    \n",
    "    return\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['iter', 't', 'dt', 'Density[kg/m3]', 'Pressure[Pascal]', 'Temperature[K]', 'H2', 'H', 'O', 'O2', 'OH', 'H2O', 'HO2', 'H2O2', 'C', 'CH', 'CH2', 'CH2(S)', 'CH3', 'CH4', 'CO', 'CO2', 'HCO', 'CH2O', 'CH2OH', 'CH3O', 'CH3OH', 'C2H', 'C2H2', 'C2H3', 'C2H4', 'C2H5', 'C2H6', 'HCCO', 'CH2CO', 'HCCOH', 'N', 'NH', 'NH2', 'NH3', 'NNH', 'NO', 'NO2', 'N2O', 'HNO', 'CN', 'HCN', 'H2CN', 'HCNN', 'HCNO', 'HOCN', 'HNCO', 'NCO', 'N2', 'AR', 'C3H7', 'C3H8', 'CH2CHO', 'CH3CHO', '_PT_', 'O_PT', 'H2O_PT', 'CO2_PT', 'CO_PT', 'OH_PT', 'H_PT', 'CH3_PT', 'CH2_PT', 'CH_PT', 'C_PT']\n"
     ]
    }
   ],
   "source": [
    "print(Header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZM66iGzid0LOnSfX5Gq2shJ9+MiK5ZUvd45YtsGyZOe7ZU/+z0tNrxdCXggUyMxOysmqPWVlmmtYOLGIc+weyNIFWET8R6QC8BsxQ1eIGpuzqi+TbqhF+WwJVpaKiImBU5zM3yMjIaNDcwFJLYqJx3N+7d+Nt9+0zQrljR+NpzRpz3L274Xs6nUYM/QUxWCRD1aWnm9ShgxXQiGFHfpZmEHHxE5EEjPA9r6qve4u3ikgP76ivB+ALaFhfFOCwIvy63W5ycnJq8rm5ueTm5rbQK2kYf3MDn9iBjW7QmiQnm7XCvn3Dv8bthp07YdcuM3Lcvdsk33mosjVrastKShp/hogRQX9B9J2HyjfUxiem9mOEDWbbiuTl5ZGXl+fLtgsrgUjv9hTgCeAbVb3fr+pN4FfAPd7jPL/yq0VkDmbDyx6vQIYV4dflclFYWBiZFxNEZWVlwKjO39ygc+fODBw40EY3aAO4XGadsGvXxtuGwuOB4uK6QllSAqWl5uif/Ms2bAjM790b/nPT0owQ+iefONaXb6hNamobFlT7PxZx/AcSIuJupHmbINIK/jPgAuBrEVnqLfsdRvReFpFLgA3Amd66+Rgzh9UYU4eLAFo7wm8wqkppaWnMmRtYoo/TacwzsrMbb9sYbrcRw2DR9OV9daHypaVmGnf9+sA27v34mvIJ6v6IZqiyrCyzhhpxMbXTnpZmEOndnp8Qer0O4OgQ7RW4qp57PQk82XK9C48dO3awcuXKNmFuYGnbuFy1a4ctRUVFXZEMJZz1lTVVUB0Os5GoS5fA1LOncYDgS716mdfdLOz/oaUJ2KFKI2RmZnL44YeTmpoa7a5EDbfbHfej2vLycrQNjjSSkkzq1Kll7qdqduDWJ5olJWb9dNu2wLR8udmMtDNovsblMh6ADjjApJwcE2MyMzPMzlgsTSS+v9HCwOVy4XA4KCsri3ZXokZOTk6rraXGKk6nM9pdiAlEmieoe/eatc71681x7VpYsQIKC+Hll00bh8OI4JlnwllnhbGByY78LE3Aip+l5SgogPx88624Y0fd4+TJpl1+vjmfODHwOv+y4HuGqmvpfkfyGRbAeOYZNsykYEpLjZ/Yjz6Ct9+GG2+Em26C00+H224zI8MA7MjP0gys+FmaRl4ePPGEsS/IzjZW5oWFZvtjKETMT3oR08blgmuuMXWPPGIWknxlAwea8rVr668L/rXf1PyaNfDAA7XPuO46GDTIjiaiQIcOtXEjb7/d/GmefNJ8BN54A/74R7j11hAbaezfytIErPiFQbyvdwXYSublmZ/hW7bs301UA4Wxqgruvz+wTaiycOpaiqoqmDUrZJXL+kdrdQYNgrvuMr9Hrr3WiN8PP5hAy4Ad+VmaRXx/q4eJFT+v+J1/voldFA4i5svJd3Q4arf1eTzG6edrr5n86acb4fGVHXSQKV+0KHRd8Jdec/KFhfDLX9Y+46WXYPz4Ou1dw4eH97otLU6nTvDcc8Y/7MyZ5jhpkl8DO/KzNIH4/la3hM/NN4cnfE4nXH+92a8f7prfBx+EXnM74YT661qKadMi/wxLsxExU6GrVpkR4BNPpEJ3O/KzNB1pi9u36yMtLU0jsSszLS0trnd7UlAQ9FPbj8GDa3cidO8O06e3SwGJ+89AjLB9uwmbdcghW3n/0V0wYgS8+CKcfTb5+flM9v3AskQMESlX1bRo96O52JGfpXFmz65blphodiK0ku9UiwVMJI4LL4R//KMLu3bvpgUc61jilLbqzc/SmixcWLfMCp8lSpx/PlRVOfjPxx1MgV3zszQBK36WhikogKVLA8u6d7fCZ4kaOTmQmurm48Up0e6KpQ1jxc/SMPfeW7fs0ENbvx8WixenE0aP3sPHi70uB+3Iz9IErPhZGmbVqrplN93U+v2wWPwYMKCM79Yl4sFhxc/SJKz4WRqmS5fA/Nix7XI3p6Vt0bt3OVVuB+vpF+2uWNooVvwsDdOxY2C+f/+odMNi8adXLxP5dzWD7cjP0iSs+FkaZvv2wHxwTBqLJQp07lwJwFa6RbknlraKFT9Lw/z0U2B+27bo9MNi8SMrqwqAn+hqR36WJmHFz9IwwV8swWuAFksUSEtzk+CqZhv282hpGlb8LPVTUADffRdYNnJkdPpisfghAl2yPXbkZ2kyVvws9TN7dmAEBIfD+O60WGKA9DQPpXSIdjcsbZSIip+IPCkiP4nIcr+yl0RkqTetE5Gl3vL+IrLXr+5xv2smiMjXIrJaRB4WsT/1WoXgmH2HHWbNHCwxQ4fUaspIsyM/S5OI9MjvaeA4/wJV/aWqjlXVscBrwOt+1Wt8dap6uV/5Y0AuMMSbAu5piRB2Z6clhklL9oqfJaYQkeNEZJV3sHJLiPrDRWSJiLhF5IygOo/fAOjNSPYzouKnqh8BIb9BvaO3s4AXG7qHiPQAMlS1QE38pdnAKS3dV0sINmxoOG+xRJG0FDvyizVExAn8DTgeGAmcIyLBGwU2ABcCL4S4xV6/AdC0SPY1mmt+Pwe2qur3fmUDRORLEfmfiPzcW9YLKPJrU+Qts0SaxiKiWyxRJC3FrvnFIAcDq1V1rapWAnOAk/0bqOo6VV0GVEejgz6iKX7nEDjq2wz0VdVxwHXACyKSAYT6Wdeq38JVVZ0oKGjNJ8YI2dkN5y2WKGJHflHDJSKFfsk/xEsvYKNffn8HK8neey4UkYjO8EUlmK2IuIDTgAm+MlWtACq854tFZA0wFPPm9fa7vDewKdR93W43OTk5Nfnc3FxyWyD0TlVVV444Aq65BsaMgfT0wPTdd/Dll3DssfDzn9e9vqAA8vNh8mSzXyQ4H7NUVDSct1iiSEpiNftIjnY34oK8vDzy8vJq8qqaU0/T5g5W+qrqJhEZCHwgIl+r6pr9uD5sohXJ/RjgW1Wtmc4UkS7ATlX1eF/4EGCtqu4UkRIRORT4HJgOPBLqpi6Xi8LCwoh0uKoK7r+/4TZ33mkCnGdm1gqjKixfDtXV4HLBccfBu++Cx2Pyf/ubab98OUydCpMmxZBYJiU1nLdYokiCS6kiwY78WgH/gYSIuBtoWgT08cvXO1gJhapu8h7Xikg+MA5oe+InIi8Ck4HOIlIE3KaqTwBnU3ejy+HATO8b6wEuV1XfZpkrMDtHU4D/eFMroqSkwCuvwPDhUFJSm559Fl56yYicCBxyiLED99X7hA/A7Ya33qq9a2UlXHZZbX7mTOjc2Wyy9InliSfC228bsUxMhPfeM/HMfGIIERTG3bsbzlssUaRG/CyxxCJgiIgMAH7EfNefG86FIpINlKtqhYh0Bn4GhAgo2jJEVPxU9Zx6yi8MUfYaxvQhVPtC4IAW7dx+kJCwjQULQotLdjbMm2eELDERZs0KbFdQAEcfXVv/17/CddeZkaTLBUcdZUaC1dVGPF2uQLF8443ae+3bB0ccYYRW1YigSK1QXnQRjB8PO3a0kBhu2tRw3mKJIgnOajvyizFU1S0iVwPvAk7gSVVdISIzgUJVfVNEDgLmAtnASSJyh6qOAkYA/xCRasx+lHtUdWWk+hqtac82RULCjnqFZOJEWLCg/tFXqPqxY+uO3HzieMcdMGNGbf7ee+HGG03e6YRRo2DpUnOdx1P7nMpK+Mc/avMuF1xwgQm63mQxrKxsOG+xRBE77RmbqOp8YH5Q2R/9zhcRuI/DV/4ZMDriHfRixa8FmDixYWEJrg/OB4vj6NGB+QkTAsXSN5J0Ok2+qqquFYLbDU89ZRKY5bqHH27BUaHFEmUSXIriwOMxQwyLZX+w4hcDNCaODYklGBecTz1lRNA3fRoshhUV8Otfm3OnE046CW66yYqgpe2S4DIf8qpqpxU/y35jxa8NEkocp083gtipkzG78BdDh8MIom+a1OMxa4nz5sHJJzcggqmpUF4emLdYYgSXwyyOV7nFGjzEISJyGjAL6IoxsRBAVTUjnOut+LUTggXRXwx37DDHq64y06E+VI0IvvkmTJsWQgSdQb+ng/MWSxTxjfzc1TY4TZxyL3CSqn7TlIut+LVT6luHvPLKwI0yYEaHb7wB//43/P3vUOMXwB1kzhOct1iiSM20p8eKX5yytanCBzaeX1yRmwsffwynnGKmQoPxeODyy+Hmm70FduRniWESXN5pTyt+8UqhN0TeOSJymi+Fe7H91MQZEyfC3LnwySehRVDVmFfcfDPQo0dgZXDeYokiPgMHDelRyxIHZADlwBTgJG86MdyL7bRnnOITwYICI3b+xvRgygZ1PI1cZtUWVlW1bictlgZwiJn2tOIXn6jqRc253o784hyfCN50U926y3f+iQIOrS0oLm69jlksjSBe8auOamAcS7QQkd4iMldEfhKRrSLymojUMZ6vDyt+FsC4ZTv88MAyxcmV/K22wI78LDGEwzvgsyO/uOUp4E2gJyZs0r+9ZWFhxc9Swz331PUUtZRx5HGpyaSltX6nLJZ68E17VqsVvzili6o+papub3oa6BLuxVb8LDVMnGj8iNZivlT+j7+abHp6q/fJYqkP3w81K35xy3YROV9EnN50PrAj3Ivr3fAiIq+Hcf1OVb003IdZYp9Zs8xmF3/KSSePS8ndHjLohsUSFeyGl7jnYuBR4AFMwNzPvGVh0dBuz9HA5Q3UC/BQuA+ytFUEUG5gFrlJbzXa2mJpLQQ77RnPqOoGYFpTr29I/G5T1QUNXSwidzX1wZbYpV8/WL8ezI8p88VSQgZ07x7NblksAdiRX3wiIjep6r0i8gigwfWqem0492lI/JY18PDLVPWfqvpCOA+xtC3Wrau78SUBj43kbokp7MgvbvG5NCtszk0a2vDypoiMCy4UkT8AVzfnoZbYZ8iQwHx/1plQ8hZLjODzTmRHfvGFqv7be1quqs/4J4zHl7BoSPzOBl4TkYN9BSLyKHAMcERTOm1pO7iC5gQqrTMgS4zhsEbu8c5vwywLSb3faKr6hddJ6OsikgtcBiQBU1XVDgHaOV26wDd+/tI30J+CygnY2LeWWKHGw4u12IorROR44ASgl4g87FeVAYQdeqbeT42IZABrgfOBOZiFxYuARG9dOJ180ut6Zrlf2e0i8qOILPWmE/zqfisiq0VklYhM9Ss/zlu2WkRuCffFWZrOyJH+OUFxMLvy7Gh1xxIhduzYwdixYxk7dizdu3enV69eNfnKyspod69BHN41P62z5cHSztmEWe/bByz2S28CUxu4LoCG5rJWULuTZi/wM+ArfHvfoW8Y938aY4cxO6j8AVX9i3+BiIzETLWOwrireV9Ehnqr/wYcCxQBi0TkTVVdGcbzLU1k+riveZwR+H9EVjpGRa9DlojQqVMnli5dCsDtt99Ohw4duOGGG6Lcq/CwRu7xiap+BXwlInOBMlX1AIiIEzM7GRb1jvxUtY+q9vWmPn75PqoajvChqh8BO8Psy8nAHFWtUNUfgNXAwd60WlXXqmolZhR6cpj3tDSRif/5I93ZGlC2XsP2GWtpBzzzzDMcfPDBjB07liuvvJLq6mrcbjdZWVnceOONjB8/nqlTp/L5559zxBFHMHDgQObPnw/Av/71L0499VSmTp3KsGHD+NOf/tTi/as1dbDEKf8FUvzyKcD74V7c0LRn58YuDqdNPVwtIsu806LZ3rJewEa/NkXesvrKLZFk1SqS2Yf/V4ukdYhefyytyvLly5k7dy6fffYZS5cuxe12M2fOHAD27NnDlClTWLJkCYmJidx+++0sWLCAV155hT/+8Y819/jiiy+YM2cOS5Ys4YUXXqgZYbYUtb497ZpfnJKsqqW+jPc8NdyLG5r2/C8wvpHrw2kTzGPAnZhv1TuBv2Jc0oSau1BCC3TIH3tut5ucnJyafG5uLrm5ufvZPQsAbjd92cg6BtYU9R2a0sAFlmYzYwa0sEAwdiw8+OB+X/b++++zaNGimv+nvXv30qdPHwBSUlI49thjARg9ejSZmZm4XC5Gjx7NunXrau4xdepUsrPNb9tTTjmFTz75hLFjxzbzBdVSY+dnd3tGnLy8PPLy8nzZWNn6XSYi41V1CYCITMAs0YVFQy/iQBFpaMpS2A+bCh+qWjOXJiL/BHw+s4qAPn5Ne2MWNmmgPACXy0VhYbPsHi0+vnPxlMAAACAASURBVP8+2j2wRBFV5eKLL+bOO+8MKHe73SQmJtbkHQ4HSUlJNedud+1mOwnylBCcby4+Oz+72zPy+A8kRCTsHZURZgbwioj49KAH8MtwL25I/BIbqPOx39PtItJDVTd7s6cCvp2gbwIviMj9mA0vQ4AvMCI7REQGAD9iNsWcu7/PtewH/fsDsD7gN4fP5ZklYjRhhBYpjjnmGM444wx+85vf0LlzZ3bs2EFZWRk9e/YM+x7//e9/2b17N4mJicybN4/nn3++RfsodrUvrlHVRSIyHBiG0YlvVTXsoKMN2fl5mts5EXkRmAx0FpEi4DZgsoiMxQjnOuDX3uetEJGXgZUYW42r/HbxXA28CziBJ1V1RXP7ZmkAr8oF/05v4R/ulhhm9OjR3HbbbRxzzDFUV1eTkJDA448/vl/id9hhh3HuueeyZs0aLrjgghad8rRYvAwDRgLJwDgRQVWDrQtCEtG5W1U9J0TxEw20vwuo4yxbVecD81uwa5b6KCioOc1iT0BVVlZrd8bSmtx+++0B+XPPPZdzz607ybLbz8er/y5Ol8sVUNetWzdefPHFlu+oD2vgF9eIyG2YwdVIjD4cD3xCXdO6kNjJcksgV1xRc7qLTL8KYdeu1u+OxVIvPvGzUxLxyhnA0cAWVb0IOJD9sPMLa+QnImOAwzBTlZ+qar0RHyxtmPPPh6++qsnaaU9LU7n00laMcW0/mPHKXlWtFhG31+vYT+C3Pb0RGh35icitwIsY27remE0pYTsPtbQRbr4ZgjYk2GlPi8USwxSKSBbwT4x7syWYTZJhEc7I73xggqqWQ00A28XAn/e/r5aYo6AAZs2CefPqVO1K7Q3ltb+q7bSnJaaw055xixi7mT+r6m7gcRF5B8jYn1nJcNb81hMoki6Mw2tLW6agAE49FQ47LKTwcd55VGR0DSiqqGilvlks4VAjftHthqX1UVUF3vDLr9vf5bhwxK8cWCEi//IapX8N7BaR+702eZa2hL/ovfFGXfcYInDTTfDccyQnB1YF5y2WWMAGs41bForIQU29OJxpz7e9qeaBTX2YJQoUFEB+PkyeDIsWwf/9X/3+oJxO+PvfwevJIXiNz675WWKJWiN3K35xypHAr0VkPVCGN+KQqo4J5+JGxU9V67XLs8QgPrHr1AmWLIGnnoIqr9OD+uyiHA6YNs2M+CbWhqsNnua0056WmMKu+cUlIjLAG/nn+Obcp1HxE5HjMA6o+3nb+9S1Y3MebGkG/qM5MOdZWfC//8Hrr9eKXWPUI3o+kpIazlvaD0VFRVx11VWsXLmS6upqTjzxRO67774AP54xhxW/eOVVYALG29fRTb1JONOejwJnYdb6rP/01sBf3CZONPkPP4QJE2DzZvj1r43AORzmC6Axt/YikJBgjlVV5njSSfWKno/g3Z12t2f7RFU57bTTuOKKK5g3bx4ej4fc3FxuvfVW7rvvvmh3r36s+MUrDq93l6Eicl1wpaqGtRclHPErApaqqhW++ggWq8bqffmJE2H4cCNs+fnQr59RmAcfBI/HiNvAgbB6degpS08j7lcdDnC54OKLYfp0U9ZQP4Ow057xwQcffEBycjIXXXQRAE6nkwceeIABAwZwxx13kJoadog0i8U3W/gQxhfzv1T1nqD6w4EHgTHA2ar6ql/dr4Dfe7N/UtVnQjzibOAUjH6lN7Wf4YjfTcC/RSQfqPn6U9WHm/rQNkdVlRGsnBwoLYWystrj55+bTSRut9kwcvXV0KVLbZu1a2H+/Fox69oVtmwJ77keD2zbFvgL99hjzfSm73m+0Vx1daDYjRsHO3bUFbowRM+H3e0ZH6xYsYIJEyYElGVkZNC3b19Wr17NmDFh7R9ofezIL+YQESfwN+BYzMBpkYi8qaor/ZptAC4Ebgi6tiMm+EEOxpvYYu+1AXNOqroKmCUiy1T1P03tazjidwdQBWQRr9OeVVUwaVLj7aqr4YEHzLnDAR06mH9Q3witutrkRWqPw4fDqlWmzumEc8+FV1+FykpITDQG6DNm1OZ9zoeD1/w6dQotds3A7vZsXaIVy1ZVQ8baU1XmzZvHI488wk8//cRVV13FlClTWraDzcGKXyxyMLBaVdcCiMgc4GRMtB7A2OR564L1ZCrwnqru9Na/BxyH8TBWh+YIH4Qnfl1VdULjzdo5InDMMXDCCUbU0tLMcd06uPFGMxJLSDAbTo480uwOETEjxqOPrhWvmTMDxWzGjMD8FVeY5D89OXp03enKJo7m9gc77RkfjBo1itdeey2grLi4mI0bN3L99deTmprKrl27uOGGG2JL/LDiF4P0Ajb65YuAQ5pxba8W6lcdwhG/BSJylKp+EKlOtAmSk+GOO0ILTU5O/WtpEyfCggUNi1k44hYhgWsIu9uzdYlWLNujjz6aW265hdmzZzN9+nQ8Hg/XX389F154Yc1635/+9Ceuuuqq6HSwEayRe6vjEpFCv3yequZ5z0P9McKNPdWca/ebcMTvMuAGESkHKolHU4eEBCNg9QlQY+IUXN9YPkawI7/4QESYO3cuV155JXfeeSfV1dWccMIJ3H333agqt9xyC8cffzzjx4+PdlcDEBvPL1q4VTWnnroioI9fvjewKcz7FmHi8/lfm19fYxE5E3hHVUtE5PfAeMwmmSXhPCwc8esczo3aNQkJMSlOkcaO/OKHPn368O9//7tO+cMPP8z777/Pnj17WL16NZdffnkUemdpQywChojIAOBHzM7MuhGRQ/MucLeIZHvzU4CGIgj9QVVfEZHDMOuFfwEeI8xp1nA8vHhE5GxgoKreLSK9gW6YyA6Wdowd+VmuvfZarr322mh3IzR2w0vMoapuEbkaI2ROjCH6ChGZCRSq6ptef5xzgWzgJBG5Q1VHqepOEbkTI6AAM32bX+rBZ+v1C+AxVZ0nIreH29dwPLw8CiQAhwN3YxxdPw402aGopW1QUtJw3mKJKlb8YhJVnQ/MDyr7o9/5IsyUZqhrnwSeDPNRP4rIP4BjMKYPSYQXrAHCbDhJVX8N7PN2bicQls8jEXlSRH4SkeV+ZfeJyLciskxE5nqDESIi/UVkr4gs9abH/a6ZICJfi8hqEXlYQu3LtrQ4mzY1nLdYoosVvzjnLMwI8zhvXL+OwI3hXhyO+FWJiAPvJ01EOhG+vd/TGDsNf94DDvB63v6OwDndNao61pv8FxceA3KBId4UfM+IUplcyevfvM6mkk3sKN9BSUUJFe4Kqr1Obwo2FvDnj/9MwcaCkNcH1+9vPloEe01rzIuaxdKq2P0u8U4P4G1V/V5EJgNn0hKR3EXEpapujLX+a0AXEbkDo7Z3hHNzVf1IRPoHlf3XL7sQOKOhe4hID0yE3gJvfjbGtU2zDBz3B3eSm9NfPj1knVOceLTWzViX1C6kJ6WT6Ewk0ZlIpaeS73Z8R7VW4xAHo7qMYuW2lXjUg1OcHNHvCD7a8BGeag8uh4szR53JqytexV3txuV0cf+U+0l2JbNi2wqOGnAUxww8hi83f0n+unwm958MQP66fDqldmJH+Y6a4+T+k5nYJ/426VjiCDvtGe+8BuSIyGDgCeBN4AXghHAubmjN7wtgvKrOFpHFmHlVAc5U1eUNXLc/XAy85JcfICJfAsXA71X1Y4yRY5Ffm4gaPtaHAwfThk3jqAFHUemprEn56/L53/r/oSiC0DezLyO6jKipX7ltZc0IsVqrWbd7XY1YetTD/9b/ryZfVV3FC1+/UPPMSk8lV//n6pr8AwsfqNMvQdCgn8CC4HK4OGX4KUzsNZG9nr0c2f9IAGZ/NRuA6QdOb1QcfY5o/PMWS8xgxS/eqfZusDkNeFBVH/HqR1g0JH41nyhVXQGsaEYn695c5FbADTzvLdoM9FXVHSIyAXhDREbRyoaP9ZHkSuKmn91URzCmbJzC0bOPptJTSaIzkUeOfySgTcHGgoD6v0z5CzPemVGTf/C4BwPyM4+cyR8+/ANVnipcDhdTBk3h7e/epppqHDgYmD2QNbvW1AhesPD5yqqqq3hl5Su8svKVmnJ/ocxbnMdhfQ+jY0pHunfoHlIMU1KgvDwwb7HEGtbIPW6pEpFzgOnASd6yhHAvbkj8uoQKF+Ej3LARofB67j4ROFrV/HxT1Qq8jrNVdbGIrAGGYkZ6/juD6jWadLvd5OTU2l7m5uaS641K3hwS9iWwYPqCkCOliX0msmD6gpppyOA2oepHdx3dYP5nfX4WMK35/tr3a8Txxp/dWCOWTocTQajyVFFNdY24hRoNQqBQVlPNRxs+qsnnLc5jTPcxJDoSuWT8JeROyCUjI1D8MjKa+UZaLC2IVHuXG5zO6HYkDsjLyyMvz+fEJSz78NbgIuBy4C5V/cFrW/hcuBeL1uMlQUQ2YzaahPxZpaphrft51/zeUtUDvPnjgPuBI1R1m1+7LsBOr13hQOBjYLTX9mMRcA3wOWYL7SPe7bQBpKWlaVlZWTjd2i/S0tKIxH3DpWBjQYA4+ueh7prfl5u/5KmlT9WIokMcuBwuVM2IMBwO7HYgax95jJKVtWLesaPxnR2PRPszYAkkPz8f99sVHPuXqXz88mYOO7MH+fn5TPY5e7dEDBEpV9W0aPejuTSk4JtVdWZzbi4iL2Lc1XQWkSJMuIrfAknAe16LhYXenZ2HAzNFxI0xXrzcz8DxCszO0RTMRpdW2+wSC0zsMzFgRBkqH8z0A6cHiKJPKO/99F7eXPUm1Y1s2P1q61dwxiRYPQVeeBcwUZoslpjBY0d+8YyIDAH+DIwEagKuqerAcK4Pa82vqajqOSGKn6in7WuY3Tuh6gqBA5rbn3giWCB9zD17LgUbC5j91Wy2lG5h596dfLLhk9BiKMCQ/8JNGXBvsfXw0o7ZsmULM2bMYNGiRSQlJdG/f38efPBBxo8fT2lpaU27p59+msLCQh599NEo9taLFb945ynMgOoB4EjMNGjYutWQ+B3dvH5ZYpVgYfSJ4YK1C/h+1/e1DQWztSilBC4fCY+v5Pzz4bmwZ9UtbQFV5dRTT+VXv/oVc+bMAWDp0qVs3bo1yj1rBCt+8U6Kqi4QEVHV9cDtIvIxRhAbpV7xa8SnmqUd4S+G579+Ps9//XxtpU8Au3wDwJw5VvzaGx9++CEJCQkBTqvHjh0bxR6FiRW/eGef1wHL915/oj8CXcO9OFZ27VhihOdOe45XV75KhSdojtMB9C7AUzSRm282AeYtLcuMd2awdEvLhnIf230sDx7XcKDA5cuXM2FC6HjVe/fuDRDCnTt3Mm3atBbtY5Ox4hfvzABSgWuBO4GjgF+Fe3HYTkAt8cO+3+8LLPDNoh99CwD33gtHHGGC1FvaNykpKSxdurQmzZwZuAeurKyMCRMm8NZbb7V+57ymDuqw4hePqOoiVS1V1SJVvUhVT1PVheFeb0d+lpDc9LObuPfTe82Up0/8etV+rj76CCZNgrFjoX//2uu6d4dx44xJRKdOgUffLvTgoPUFBXXLfDRU11K0xjPCobERWqQYNWoUr776apOunTVrFmeddVYL9yg8xDfyc1nxiydE5M2G6lU1rKkJK36WkMw6ZpYRP/+9U47KOu2WLjUpHHyzU9XV5vz8841nqmefNTNYTidMnw59+pjyjRvhmWdq6y68EPr1q/Vm1RLHdevg8cfB7QaXC668EgYMCN2+vXLUUUfxu9/9jn/+859cdtllACxatIhyfw8HIXj//fcZOXIk+/bta7BdxPCJnx35xRsTgY3Aixjb7yb9h1rxs4RPM0XAU+v/G7cbnn46sN7thifrieTldsO//tW85zdGVRU89FDoutTUyD47mogIc+fOZcaMGdxzzz0kJyfXmDo0xIcffkhZWRkrV64kJSWFE044AYejFVdSfB+o9v7rxBJMd+BY4BxMlPi3gRe9bjjDxoqfJTzEpMxM2LMnjOZep9i+o8NhRlYiRsgSE2HBAtP26KOhsrK27NBDTflnn8Gxx9bWvftubZ3PMVFzj59/DiefXPuMuXPh4IMD26maEWd7pmfPnrz88st1yv1t/AAuvPBCLrzwQgDuuusuwNj+de7cuXWFD8wHyRJ3qKoHeAd4xxvA9hwgX0Rmquoj4d7Hip8lfBxQ8n9OUp1pePam4alMJrWyL6nSsaZJx8TujOsxjm1lO+iS1ingePqEyQC8tjif0ydMZqJ3ge3B1wrqlAH87GeBdT//ecsvyB17bOAzpk61YaD2F58YtjrRmm61RB2v6P0CI3z9gYeB1/fnHlb8LPtFNdWUe0ogsQQSoZh1FPvVbwFW+gqCjv/9ojayxHtfCA+sHQbAqu2rAso6JHYAoLSytN66lqI1nmGJENblUFwiIs9gPH79B7ijqSH2rPhZWhX/UEy+OIfBZV3TjJ1qcUVxvXUtRbjPWMnKFn2upQWw4hevXACUYaL+XCu1a74CqKqGFX/Gip8lIgSHV/JFlhAEd7WbRGciT5/8NEBAvMOnT366xttMcCxE/7qWItxnpF3W5p3Ytz/27Y12DyxRQFVbZHHZil8YuON0YV1vU+SOwJ10DhykJaaRlphGsiuZvpl96Zhcu+bXvYNZ8/OFV/I/+odg8o99WF88xMZiJbYE4T4jXj8DMc0+M/KrJyqbxdIgVvzCIJ6/+PQ2JScnh8LCwha7Z6iAv/WJTkN1Ldmfxp4Rz5+BWEUq7IYXS9Ox7s0slkZQVVSVkpKSaHfF4k8jRvgWS0PYkV8YVFdXk5YWv2s+brc7rl8/GAFctmwZ/fr1o1evXog1rI4+xWEYnFos9WDFLwzULipYMBEOVqxYwe7duxk2bBgJCQnR7lL8Ul0Ne4obb2ex1IOd9rRYwiQlJYXx48eTlJREYWEhe8JxdWOJCK7S0pqoDhZLU4hb8du4cSNHHnkkI0aMYNSoUTwUwqnjt99+y8SJE0lKSuIvf/lLQN0DDzzAqFGjOOCAAzjnnHOi59y3GYTzHjz//POMGTOGMWPGMGnSJL766quaunfeeYdhw4YxePBg7rnnntbseouxv+/BYYcdRnl5OYMHD2bBggVMnDixwWstkSE51qPMW2If32J+e0ipqakaLps2bdLFixerqmpxcbEOGTJEV6xYEdBm69at+sUXX+jvfvc7ve+++2rKi4qKtH///lpeXq6qqmeeeaY+9dRTYT87VgjnPfj00091586dqqo6f/58Pfjgg1VV1e1268CBA3XNmjVaUVGhY8aMqXNtW6A578HatWv1ueee06+++kq3b98e8lpLZFh+2226gCMVVP/3P1P24YcfRrVP8QJQpjHwfd/cFPGRn4g8KSI/ichyv7KOIvKeiHzvPWZ7y0VEHhaR1SKyTETG+13zK2/770Uk7Gi99dGjRw/Gjze3T09PZ8SIEfz4448Bbbp27cpBBx0Ucm3H7Xazd+9e3G435eXl9OzZs7ldanXCeQ8mTZpEdnY2AIceeihFRUUAfPHFFwwePJiBAweSmJjI2Wefzbx581r3BbQAzXkPBgwYwDnnnENaWhrfffcdgwcPrnOtJTKkeP8GFktTaY1pz6eB44LKbgEWqOoQYIE3D3A8MMSbcoHHwIglcBtwCHAwcJtPMFuCdevW8eWXX3LIIYeE1b5Xr17ccMMN9O3blx49epCZmcmUKVNaqjtRIZz34IknnuD4448H4Mcff6RPnz41db17927zX/z7+x4AOBwOBg0aRHJyMosXL6ZHjx52g1QrkL5qFfQwPzjt221pChEXP1X9CNgZVHwy8Iz3/BngFL/y2d7R9UIgS0R6AFOB91R1p6ruAt6jrqA2idLSUk4//XQefPBBMjLCcgnHrl27mDdvHj/88AObNm2irKyM5557riW6ExXCeQ8+/PBDnnjiCWbNmgWE3gHblrf/N+U98L/20ksv5aGHHqKqqoply5ZRWVk38K+lhfB4yFy+HDlgVLR7YmnDRGvDSzdV3QzgPfo8CffCROj1UeQtq6+8WVRVVXH66adz3nnncdppp4V93fvvv8+AAQPo0qULCQkJnHbaaXz22WfN7U5UCOc9WLZsGZdeeinz5s2jU6dOgBnpbdxY+ycpKipqk1O/0PT3IPjas88+mwMPPJCMjAwKCwvZtWtXa72E+OKjj0jcvRsm2vBTlqYTa7s9Qw0dtIHyJqOqXHLJJYwYMYLrrrtuv67t27cvCxcupLy8HFVlwYIFjBgxojndiQrhvAcbNmzgtNNO49lnn2Xo0KE15QcddBDff/89P/zwA5WVlcyZM4dp06a1VtdbjOa8B6GudTgcDBgwgOHDh/PNN9/www8/2GnQluaxx3CnptZGNrZYmkC0jNy3ikgPVd3sndb8yVteBPTxa9cb2OQtnxxUnh98U7fbTU5OTk0+NzeX3NzckB349NNPefbZZxk9ejRjx44F4O6772bDhg0AXH755WzZsoWcnByKi4txOBw8+OCDrFy5kkMOOYQzzjiD8ePH43K5GDduXL3PiWXCeQ9mzpzJjh07uPLKKwFwuVwUFhbicrl49NFHmTp1Kh6Ph4svvphRo9reNFRz3oP6rj3hhBPo2LEjEyZM4JtvvuGrr75ixIgRJCUlRedFtic++gheeYWiCy6A5ORo9yZuyMvLIy8vz5dtF85RpDV+lYpIf+AtVT3Am78P2KGq94jILUBHVb1JRH4BXA2cgNnc8rCqHuzd8LIY8O3+XAJMUNWAtcS0tDQtKyuL+OuxWMJFVVm/fj2bNm1i+PDhdOzYsfGLLKFZuRImT4aMDD5+5BHcycdz1FGQnw9HHAH5+flMnjw5yp1s/4hIuaq2eX+HrWHq8CJQAAwTkSIRuQS4BzhWRL4HjvXmAeYDa4HVwD+BKwG8IncnsMibZgYLn8USi4gI/fv3Z+TIkXz77besXbuW6urqaHerbaEKr7wChx0GTif85z94UlKi3StLGyfiw1dVPaeeqqNDtFXgqnru8yTwZEPPqkqq4pHPH8EhDkQEQZp9npGUQbcO3eiW1o3OqZ1xOpz7/R5YLFlZWeTk5PDtt9+ydOlSRo4cSbKdtmuY8nKYOxf+/nf47DOYMAHmzIHBg6GNm9VYok+7mLv1UZVcxbXvXBux+zvEQefUznRL60avjF70yehjUmbtsXdGb1ITUiPWB0vbJTExkdGjR7Nx40YWL17MsGHD6Ny5c7S7FTuUl8OKFVBQAB98YFJJCQwYAI89BpdcAiEcTtj9RJam0K7EL6U4hQ03bkBVqdZqFG3WebVWs2ffHraWbWVL6Ra2lm6tOf+x5EcWb1rMtvJtdfrRMaUj/TL7MajjIAZle1PHQQzMHkifjD529BjHiAh9+/YlMzOTlStXsnv3bgYOHIjDEWsbryNAdTXs3AmbNsG6dbB+vUk//ADLl8P339cq2aBBcM45cO658POfQ4j3pw2blbZrROQ44CHACfxLVe8Jqk8CZgMTgB3AL1V1nXdvyDfAKm/Thap6eaT62a7ET1TonNq6v6T3ufdRVFxEUXERG/dsZGPxRoqKi1i3ex3Lti5j3rfzqKquqmmf4Eigf1Z/BnUcxODswQzrPIxhnYYxvPNwemf0btOG4pbwyczMrJkG/fLLLxk5ciQpsb6OpQr79kFxsRmRlZQEnvvnd+2Cbdvgp59M2rYNtm8HT1AkhuRk6N8fDjjACN2YMTB+PPTrt1/dssQGIuIE/obZy1EELBKRN1V1pV+zS4BdqjpYRM4GZgG/9NatUdWxrdHXdiV+0SDZlczgjoMZ3HFwyHpPtYei4iLW7FrDmp1rzHHXGtbuWsunGz6lpLI2OnhqQirDOg1jWOdhDO80vEYYh3YaSlpim99cZQkiISGBAw44gKKiIpYsWcLQoUPp0qVLyz6kuhrKyhoXq4bO/fPB4lUfmZnQtSt06WLW6CZNMuddu0KPHkbw+vUzZU38wee7zIpfTHEwsFpV1wKIyByM5y5/8TsZuN17/irwqEThV78VvwjjdDjpl9WPfln9OGrAUQF1qsqW0i18u/1bVu1Yxartq/h2x7d8XvQ5Ly1/CfWz4++T0YfhnYfXiqP33I4W2zYiQp8+fWqmQXft2sWgQYNwVlYawfFPPhFqqDxYrEpLw1MHhwPS0yEjwxx95z171i0P1c7/PC0t5DRly793EX+EZf8J5Y0r2FluTRtVdYvIHsDnNmmAiHwJFAO/V9WPI9VRK35RRETokd6DHuk9OHLAkQF1e6v2snrn6hph9B2f/uppSitLa9qlJaQxtNPQOsI4tNNQu/EmWrjdsGePmfoLTrt31577CVhGcTET3G5WXXABX3buzMg77iC1sR2NTqcRG5/4ZGRAx45mVBWOSPmfp6S0WTWxI79WxyUihX75PFX1WcCH442rvjabgb6qukNEJgBviMgoVS1ufpfrYsUvRklJSGF0t9GM7jY6oFxV2Vy62Yjh9lU1wlhQVMCc5XMCRot9M/vWrCfWHDsPo1d6LztaDAe322zQ2L69ds3Kl3burCtmvlRS0vB9k5IgK8sknwgNGkRCRgajNm1iU1YWXz71FIO3bqUb1BU4X0pObrOC1RLYac+o4VbVnHrq6vPSFapNkYi4gExgp9fUrQJAVReLyBpgKFBIBLDi18YQEXqm96Rnes8606h7q/by/c7va4Tx2x3m+NTSp+qMFod1HsbA7IH06NDDpPTaY9e0rmQnZ5PkamfuuKqqzOaLLVtMChY0//y2bUbI6iM1FbKza1PfvnDggYFlvpSVFZhvYGOLYOaEMkpKzG7QrCwGDx6M02l3CAcTx7ofyywChojIAOBH4Gzg3KA2bwK/wjg/OQP4QFVVRLpgRNAjIgMxoe3WRqqjVvzaESkJKYzpNoYx3cYElKsqm0o21U6feoVx2dZlvLv63YBNNwH3c6WQnZJNdnJ2wDErKYu0xDQ6JHYgLSGNtMQ00hK8eb/z1IRUklxJJDmTao4uh6vlR51798LGjWYL/ZYt2NxB0AAAF5pJREFUsHlzrcD5n2/fHnqYkJgInTubzRedO8O4cYF5X/LlO3Uyo7cIkp6ezoQJE/juu+9YsmQJI0eOJC3NbnoKhR35xQ7eNbyrgXcxpg5PquoKEZkJFKrqm8ATwLMishoT7u5s7+WHAzNFxA14gMsj6cmrVXx7thbWt2fTKKssY0vpFjaXbmZzyWa2lW9j195d7Nq3q/bod757327KKsvwaJg7//wQhERnYh1RDC5LdCaS4EjAJU5cFVW49lWSsLcCV/k+XGV7cZWU4yotw1VcSkLZPlzVBCZxkpCWjqtDJq70DFwZWbgys72pI67sjjizzLkzJQ2XMwGnw4nL4cIp3mMD+XDaOsXZbKFXVbZs2cLatWsZNGgQ3bt3b9b92gv5+fkkJEzmsMPg3XdhyhTr27O1aC++Pe3Iz0JaYpoxyO84KOxrVJVKTyVlVWWUVZZRWllac+5/rHBXUOmppMJTQYW7IuSxsqKciuKdVJTspqJsFxV7SyjdV46nch/uqgqqHOD2Ty7B3cFJVZbgdgpuRyJuUdzqwYPPb6YH2O1Nfuzxpg0t8941hkMczRJQX75HUg/OKD+DLUu28MGeD1DRhu8Vxj2b05+GBN/lcJHoTCQ1IbVmJiASjh3smp+lOVjxszQJETEjNVcSHVPCjFSwa5fxzP/9CuPGauVqk98UtB6enQ2Dhhj7sEGDjD1Ynz5mXa1PH7Pxox5UFXe1O2Sqqq4yR08VHvXgqfbgUQ/uajeeau+xGfkmXxvGPTZXbObRjY9yUueTOLvj2Ty96Wk27t243/2p1ug41U50JpKWkBYgiB0SO9A5tXOd1DO9J4M7Dm7UG5Jd87M0Byt+lpZHFTZsgMJCWLzYHJcvN+tvPlJTYeRIOOYYGDrUiJwvNSPsj4iQ4EwgwVnXB2R7YcuWLfTu0JsBAwbQo0eP/ZpaVdVWEfRKTyXlVeU1MwC+8/Kq8pp8cUUx3+/8noKiAraXb8dd7Q7oa4IjgUEdB3Fwr4P5ed+fc9LQk+jWoVuI19Tst9QSh1jxszSf8nJYuNAEGv38cyN227ebOpfLuK6aMsWI3ahRJvXt2yqG0O2R7t27k56eXuMbdOjQobhc4f0riwguceFyxNa/vqqyp2IP28u3s3HPRtbsWsPqnav5Zvs3zP9+PrO/mo3L4WLasGncfdTdgJ32tDSP2PoPsLQNKirg44/hvfeM4BUWGps4ESN006ZBTo5Jo0fbiNsRIC0tjfHjx7N69WoWL17MyJEjSW9gOjjWERGykrPISs5icMfBAU4fVJXlPy3n2WXP8s8l/+TAxw/khiE3cFL/ydHrsKXNY3d7WsKjqAjeegvmzzehZsrKTHiZgw6Cww83adIk49PR0qps3bqV1atX069fP3r1at8ODLaUbuGc187ho3Uf8dSkQn41ZRxvvw0nnGB3e7YWdrenpf2zaZOJoP3yyyaYKBjXWdOnw/HHw5FHQocOUe2iBbp16xYwDTps2DASQsS9aw9079Cd1896ncEPDuahb68DPrTTnpYmYRddLIHs2wcvvABHHQW9e8OMGcY58p/+ZHZmrl1rImufdJIVvhgiNTWV8ePHk5SUxOLFiykujog7xJggOyWb03udzpKd+ZC1LtrdsbRRrPhZDKtXw3XXQa9ecN55Jtjo7bfDN9/AV1/BrbfCiBF2f3kM43A4GDJkCIMGDeLrr79m48aNtKdlDX8O6egNFND3YzvyszSJqEx7isgw4CW/ooHAH4Es4DLAFx79d6o633vNbzFBED3Atar6buv1uB3z5Zcwa5aZ3nQ44NRTITfXjPzsbsw2SZcuXejQoUPNNOjw4cPb3TRo/7T+pDjT2NuzENULot0dSxskKt9uqrpKVcd6I/ZOAMqBud7qB3x1fsI3EuP/bRRwHPB3b8RgS1NZtgx+8QsTNXv+fLjxRmOb9/LLxvbOCl+bJiUlhXHjxpGSkkJhYSF79uyJdpdaFKc4/7+9ew+Oqs4SOP49nTeQpAkZJEBINARI0BBMCp2x3EUZdaQcKWvHFcfZlZ21mN0RdayaKke3yleNMzq1uqOr5SzOOIWWDxhwd+ODEZXFHWdRTEJ4hAwOz1HkGR7yhpCzf/xuQ4wJaZLuvn27z6eqK7e7b3fOLzfpk9/v/u7vUDKoHMJbredn+iUZPuGm4UrXbz3LPjOAV1X1uKpuBjbgKgabc7VjB8yaBbW1sHw5PPKIS3qPPuoqbJuUEQqFGDt2LJWVlaxdu5atW7em1DBoSV4ZFCZonTqTcpIh+c0EXulyf46IrBaR50VkqPdYT9WBRyUqwJSgCq+84i4wf/VV+PGPYeNGuO8+V3LHpKzi4mLq6upob29n9erVnDhxwu+QYiKcXQx5e63nZ/rF1+QnItnA9cDvvIeeBSqAWlxV38cju/bwcvuVj9auXXDjjfDd77qlxFatgl/8wq2hadJCbm4utbW15Ofn09jYyL6z1SoMiCFZBZDzhSU/0y9+9/yuBZpVdSeAqu5U1VOq2gk8x5mhzWiqA9PR0UF9ff3p29y5c+McfgAsXOh6e6+/7ia2fPABjB/vd1TGB6FQiAsuuIAJEybQ1tbGli1bAj0MOiQzkvyC24agmDt37unPVVLk+nC/G3EzXYY8RaREVSOrH98ArPW2G4CXReQJYCSuwu+K7m+WmZlJY2NcKt4HT3s7zJnjhjjr62HePLe2pkl7RUVF1NXV0dbWxv79+6mqqiInzsV542FwVgGETnGs8wgQ+AVHktrs2bOZPXs2AF6x2cDzrecnIoOAq4DXujz8CxFZIyKrgSuAuwFUtRVYAKwDfg/crtqPSqrpoqHB9fYWLXIXpy9fbonPfElOTg6TJk0iHA7T1NTE3r1xK5gdN7kZeQCcOHXM50hMEPnW81PVI8Cwbo/1esGOqj4CPBLvuAJt3z646y548UU3m3PJEqip8Tsqk6REhPLycgoLC2lra2PEiBGUl5cTCshlLlkhd+1ih570ORITRMH4LTd9W7zYVVR4+WW4/35XWsgSn4nC0KFDqa+v5+DBg6xatYpjx4LRk8qMJL9OS37m3FnyC7oDB+C229yy9kOHuqT30EOQne13ZCZAsrOzqampoaioiKamJtrb2/0OqU+Z1vMzA2DJL8jeecfVy/vtb+Hee13V9Lo6v6MyASUilJWVMXHiRD755BM2btxIZ2en32H1KlKQt3sFeGOiYckviA4ehB/8wFVHHzzYTWj52c8ggDP2TPIJh8PU1dVx+PBhVq5cydGjR/0OqUeRnt8p6/mZfrDkFzTvved6e88959bjXLkSpthKbya2srOzueiiixg+fDjNzc3s3r277xclmE14MQPh93V+JlqHDsE997haepWV7mL1b3zD76hMChMRSktLKSwsPF0hoqKiImlmg2bZhBczAMnxW2zO7t133czNZ5+Fu++GlhZLfCZhCgoKqKur4/jx4zQ3N3PkyBG/QwIgZLUlzQBY8ktm7e1w661w1VWQmQnvvw9PPAGDBvkdmUkzWVlZTJw4kREjRrBy5Up27drld0inddryZqYfLPklI1V46SWYMMFdt3fffW4x6ssv9zsyk8ZEhNGjR1NTU8OmTZtYv349p075t9CSeD0/W9vT9Iclv2SzZYu7Zu9734OKCmhudjX38vL8jswYAPLz86mvr6ejo4Pm5mYOHz7sSxw27GkGwpJfsjh5Eh5/3K3J+Yc/wJNPwh//6GZ2GpNkMjMzqa6uZtSoUbS0tLBjxw7fYrFhT9MfNtszGSxdCnfcAevWwXXXwTPPwJgxfkdlzFmJCCNHjqSgoIDW1lb2799PZWUlGRkZCfv+xvSX9fz89OmncNNNMG0aHD3qqjE0NFjiM4EyZMgQ6urqUFWampoSNgwayX3W8zP9YcnPD8ePw89/7ia0NDTAww9Dayt8+9tn/qKNCZDMzEyqqqooLS2lpaWF7du3x30iSk62+1s5ftySnzl3NuyZSJ2dsGCBm725eTPccIO7dKG83O/IjImJkpKS08Og+/btY9y4cWRmxudjJj/fJb8vvojL25sUZz2/RFm2DC65BG6+GQoK4O234bXXLPGZlDN48GDq6urIyMigqamJgwcPxuX75Oa6r41NytKl8Je/DGL/fnelkDF9sZ5fvDU3u/p6b74JpaUwbx7ccgskaFKAMX7IyMhg/Pjx7Ny5k9WrV1NeXs7IkSNjOkklcqnDxytg2iIAt8ZtTg6MGAHDhrl134cMcV8j29nZbs2IjIzev4ZCXz0Dkej7Jr4s+cVLY6Orq/fGGxAOw2OPuRmddr2eSSPnnXce+fn5p9cGHT9+fMyHQRf8Thl2GJYuXUdRUTU7dsCOHW6BpMOHYfdud/nsoUPu/okTcOoUdHS4r0lctcnEkSW/WPvwQ/jpT11Pb+hQN5nlzjuhsNDvyIzxxaBBg5g8eTIbN26ksbGR6upqCgoKBvy+kV7kiBHK5WUQCu1i6tTqc34f1S8nw46OrybE7kOp8b6fzEpK/I4gNiz5xUJHByxaBL/8pUt+RUUuAd5xhzu/Z0yay8jIYNy4cezatYs1a9YwZswYRo8ePaBhUCE244QibrgzTvNyTJLy9XCLyBbgIHAK6FDVehEpAuYD5cAW4G9VdZ+4v5IngenAEWCWqjb7Efdpe/bA88/D00+7a/YqKuCpp2DWLMjP9zU0Y5LR8OHDvzQMOmHCBLKysgb0nkqAuk0maSTDbM8rVLVWVeu9+z8B3lPVSuA97z7AtUCld5sNPJvwSMGNiyxeDDfeCCNHuhp7Y8e66/XWr3e9PUt8xvQqLy+PyZMnk5eXR2NjIwcOHOjX+9gKL8lJRL4lIutFZIOI/KSH53NEZL73/EciUt7luXu9x9eLyDXxjDMZO/ozgKne9jxgGXCP9/gL6q6c/VBEwiJSoqrb4x6RKnz0kbtGb8EC2LYNiothzhz4/vfhwgvjHoIxqSQUCjF27FjC4TBr166ltLSU0tLSfiU0q+qQPEQkA3gGuAr4DPhYRBpUdV2X3f4R2KeqY0VkJvAYcJOIVAMzgYnASOBdERmnqnEpHeJ38lNgiYgo8B+qOhc4L5LQVHW7iAz39h0FfNrltZ95j/Uv+am6s9qRr52dbhrY0aNnblu3wpIlsHChG9bMzoZrrnFDm9dd5+4bY/qtuLiYIUOGfGkYNDvKv6vIOb9OtemaSWQKsEFVNwGIyKu4jkvX5DcDeNDbXgg87Z3WmgG8qqrHgc0issF7v+XxCNTv5HeZqn7uJbh3RORPZ9m3p38Jv/wv35EjboZlJJl1T25d70crOxuuvtqVFbr+epu1aUyM5ebmUltby+bNm2lqaqKqqopwONzn64ryigC48oUrGZY3jJMnT5K1YmDnD82A9dRJuaS3fVS1Q0QOAMO8xz/s9tpR8QrU1+Snqp97X3eJyH/isvzOyHCmiJQAkZLRnwGlXV4+Gvi86/t1hELUR/5rFGF2TQ2za2vdFauRq1Yj293vi7hEl5d35jZ8OEyZ4q6MNcbETSgUoqKignA4TGtrK6NGjaKsrOysw6AXDr+QB/76AfYc2QPAtm3bGDUqbp+Vaa21tZXW1tbI3WwRaezy9Fxv1A6i6aT0vk80r40Z35KfiAwGQqp60Nu+GngYaABuBR71vv6395IGYI7Xjb4EOND9fF9mbi6NO3cmqgnGmBgbNmwY9fX1p4dBq6qqyMnJ6XHfjFAGD0598PT9ZcuWMXXq1MQEmm6mn9mUeXKiywTF7vrspHTZ5zMRyQQKgb1RvjZm/JzteR7wgYisAlYAb6rq73FJ7yoR+TPupOmj3v5vAZuADcBzwA8TH7IxJt5ycnKYNGkShYWFNDU1sXfvXr9DMtH7GKgUkfNFJBs3gaWh2z6RDg7Ad4Cl3kTGBmCmNxv0fNzM/hXxCtS3np93QnRSD4+3A9N6eFyB2xMQmjHGZ6FQiPPPP59wOExbWxslJSWUlZURCiXD1VmmN945vDnA20AG8LyqtorIw0CjqjYAvwFe9Ca07MUlSLz9FuAmx3QAt8drpieApNI04cGDB2uiCmkaYxLjxIkTtLW10dnZSXV1NTk5OT0OcdqwZ2KIyBFVHex3HANl/0YZY5JadnY2NTU1FBUV0djYSHt7u98hmRTg96UOxhjTJxGhrKyMwsJC2traAOjs7LRhUNNvKfWb09HR4XcICTF37ty+dwq4dGgjpEc7Y9nGcDhMXV0dAC0tLRw7dixm7z1Q6XAsPSnRabLkF0Dp8EeWDm2E9GhnrNsYWQGmuLiYpqYm9uzZE9P37690OJaelEh+KdEIY0z6GTNmDIWFhaxbt459+/b5HY4JmJSa7SkincBRv+NIgEzcVOBUlg5thPRoZ1zbmJ+fz/Tp0zPnz5/v988xHY4lQJ6qBn7UMKWSnzHGGBONwGdvY4wx5lxZ8jPGGJN2Apn8BlIpOCiiaOMsEdktIi3e7TY/4hwIEXleRHaJyNpenhcRecr7GawWkYsTHWMsRNHOqSJyoMuxvD/RMQ6UiJSKyP+ISJuItIrIXT3sE/jjGWU7U+F45orIChFZ5bXzoR72CfbnrKoG6oZbL24jcAGQDawCqrvt80PgV972TGC+33HHoY2zgKf9jnWA7fwr4GJgbS/PTwcW40qdXAp85HfMcWrnVOANv+McYBtLgIu97Xzgkx5+ZwN/PKNsZyocTwGGeNtZwEfApd32CfTnbBB7fqcrBavqCSBSKbirGcA8b3shME3OVhgs+UTTxsBT1f/FLWzbmxnAC+p8CIS9Go+BEkU7A09Vt6tqs7d9EGjjq4VIA388o2xn4HnH6JB3N8u7dZ8dGejP2SAmv54qBXf/5ftSpWAgUik4KKJpI8DfeMNHC0WktIfngy7an0Mq+Lo3xLRYRCb6HcxAeMNfk3G9ha5S6niepZ2QAsdTRDJEpAVXUPwdVe31eAbxczaIyW8glYKDIpr4XwfKVbUGeJcz/4GlkqAfx2g1A2WqOgn4d+C/fI6n30RkCLAI+JGqftH96R5eEsjj2Uc7U+J4quopVa3FFZWdIiIXdtsl0McziMnvXCoF061ScFD02UZVbVfV497d54C6BMWWSAmt7OwXVf0iMsSkqm8BWSJS7HNY50xEsnAJ4SVVfa2HXVLiePbVzlQ5nhGquh9YBnyr21OB/pwNYvIbSKXgoOizjd3OlVyPO/eQahqAv/dmCV4KHFDV7X4HFWsiMiJyrkREpuD+LgNVt8eL/zdAm6o+0ctugT+e0bQzRY7n10Qk7G3nAd8E/tRtt0B/zgZubU8dQKXgoIiyjXeKyPW45ZT24mZ/BoqIvIKbGVcsIp8BD+BOrKOqvwLews0Q3AAcAf7Bn0gHJop2fgf4ZxHpwC3PNzNIHyKey4C/A9Z454kA7gPGQEodz2jamQrHswSYJyIZuOS9QFXfSKXPWVvezBhjTNoJ4rCnMcYYMyCW/IwxxqQdS37GGGPSjiU/Y4wxaceSnzHGmLRjyc+YKInIsC4r9e8QkW1d7v9fHL5fpHLHr8+yT573/U8E+UJqYxItcNf5GeMXVW0HagFE5EHgkKr+a5y/7XxVnXOWmI4CtSKyJc5xGJNSrOdnTAyIyCHv61QReV9EFojIJyLyqIjc4tVGWyMiFd5+XxORRSLysXe7LIrvMdF7nxZvQfPKeLfLmFRlPT9jYm8SUIVb9WIT8GtVnSKu8OkdwI+AJ4F/U9UPRGQMbjWfqj7e95+AJ1X1JW/Zu4y4tcCYFGfJz5jY+ziyZqWIbASWeI+vAa7wtr8JVHcpf1YgIvlejbjeLAf+RURGA6+p6p9jH7ox6cGGPY2JveNdtju73O/kzD+cIeDrqlrr3Ub1kfhQ1Zdxi5gfBd4WkStjHLcxacOSnzH+WAKcnsgiIrV9vUBELgA2qepTuBX1a+IXnjGpzZKfMf64E6j3Jq6sw53P68tNwFqvmsAE4IV4BmhMKrOqDsYkKRGZBdSf7VKHLvtu8fbdE++4jEkF1vMzJnkdBa6N5iJ3XH3AzoRFZkzAWc/PGGNM2rGenzHGmLRjyc8YY0zaseRnjDEm7VjyM8YYk3Ys+RljjEk7/w//IU6KWUb6fQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "info={}\n",
    "info['label1'] = {'label':'Temp','units':' [K]'}\n",
    "info['label2'] = {'label':'$\\mathrm{O}_2$','units':'Mass fraction'}\n",
    "info['label3'] = {'label':'$\\mathrm{CH}_4$','units':'Mass fraction'}\n",
    "info['loc_x'] = 0.7\n",
    "info['loc_y'] = 0.6\n",
    "info['xlim'] = [0.,3]\n",
    "info['xlim2'] = [2.18,2.22]\n",
    "info['inset_x1'] = 0.15\n",
    "info['inset_y1'] = 0.37\n",
    "info['inset_x2'] = 0.3\n",
    "info['inset_y2'] = 0.35\n",
    "x  = solTchem[:,Header.index('t')]\n",
    "y1 = solTchem[:,Header.index('Temperature[K]')]\n",
    "y2 = solTchem[:,Header.index('O2')]\n",
    "y3 = solTchem[:,Header.index('CH4')]\n",
    "makefigure3(x, y1, y2, y3, info, figure_name='TempCH4O2.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "info={}\n",
    "info['xlabel'] = 'Time [s]'\n",
    "info['ylabel'] = 'Site Fraction'\n",
    "info['zoom'] = [0.55, 0.8, 0.0, 0.012]\n",
    "info['loc_x'] = 0.5\n",
    "info['loc_y'] = 0.75\n",
    "info['zoom_scale'] = 3\n",
    "info['xlim'] = [0.,3]\n",
    "info['ylim'] = [0.,1]\n",
    "info['loc_zoom'] = [0.5, 0.1, 0.47, 0.6]\n",
    "info['label1'] = {'label':'*','scale':1}\n",
    "info['label2'] = {'label':'O*','scale':1}\n",
    "info['label3'] = {'label':'CO*','scale':2e-1}\n",
    "info['add_zoom']=False\n",
    "y1 = solTchem[:,Header.index('_PT_')]\n",
    "y2 = solTchem[:,Header.index('O_PT')]\n",
    "y3 = solTchem[:,Header.index('CO_PT')]\n",
    "\n",
    "makePlotV2(x, y1, y2, y3, info, 'surface.pdf')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
